Glaucoma Diagnosis over Eye Fundus Image through Deep Features

Lima, Alan Carlos de Moura; Maia, Lucas Bezerra; Pereira, Roberto Matheus Pinheiro; Bráz, Geraldo; Almeida, João Dallyson Sousa de; Paiva, Anselmo Cardoso de

doi:10.1109/iwssip.2018.8439477

Cited by 17 publications

(2 citation statements)

References 12 publications

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“…Generally, existing OD [10][11][12] detection methods can be categorized into two main approaches, which are appearancebased methods which use the OD features [13,14] extracted from size, intensity, and colour; and model-based methods which use the geometric correlation that exists between the OD position and the retinal vascular structure in the retina. Appearance-based methods define the OD location as the location of a brightest circular object in the retinal image using the significant OD features.…”

Section: Introductionmentioning

confidence: 99%

Detection of Glaucoma Using Optic Disk Segmentation Based on CNN and VAE Models

Kanagala¹,

Krishnaiah²

2020

ISI

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The leading eye disease commonly humans suffering is Glaucoma, which cause vision loss by progressively affecting peripheral vision. Presently, glaucoma diagnosis is performed by ophthalmologists, doctors, human professionals with the help of different medical equipment to analyze and understand for identifying the problematic eye images. Detecting glaucoma is a difficult task from the eye images with the help of existing data mining techniques. In this paper, we present an approach for diagnosing of glaucoma with the help of deep learning based techniques fundus images that are analyzed by making use of stateof-the-art deep learning techniques. Specifically, our approach towards glaucoma diagnosis using Convolutional Neural Networks (CNNs) and Varational auto encoder are used. The performance results show the comparative performance of proposed mechanism with state of art mechanisms.

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Section: Introductionmentioning

confidence: 99%

Detection of Glaucoma Using Optic Disk Segmentation Based on CNN and VAE Models

Kanagala¹,

Krishnaiah²

2020

ISI

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“…Dataset Zhang et al [9] Ran et al [75] Shaharum et al [95] Abbas et al [71], Yu et al [58], Diaz et al [39], De et al [42], Gomez et al [44] Yang et al [76], Pratap et al [50], Doshi et al [51] Sahlsten et al [49] Sisodia et al [78] Antal et al [79] Orlando et al [72] Almazroa et al [82] Chen et al [62], Ceretinia et al [40], Perdomo et al [26] Orlando et al [41] Phan et al [35] Dong et al [70] Asaoka et al [37], An et al [38], Nguyen et al [31], Xu et al [56] Pal et al [64] Hemanth et al [61] Gondal et al [19] Mansour et al [21] Quellec et al [22] Van et al [28] Umapathy et al [30] Diaz et al [39] Gargeya et al [52], Ghosh et al [53] Jiang et al [54] Yang et al [57] Pires et al [60] Singh et al [47] Singh et al [48] Legend…”

Section: Messidor-2mentioning

confidence: 99%

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

et al. 2020

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Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This paper presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes.

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Artificial Intelligence Based Glaucoma Detection

Kaur

Khosla

2019

Algorithms for Intelligent Systems

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Glaucoma Diagnosis over Eye Fundus Image through Deep Features

Cited by 17 publications

References 12 publications

Detection of Glaucoma Using Optic Disk Segmentation Based on CNN and VAE Models

Detection of Glaucoma Using Optic Disk Segmentation Based on CNN and VAE Models

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

Artificial Intelligence Based Glaucoma Detection

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